/ Abstract (Limit: 1 page) Our proposal addresses the following challenge area: 06-LM-101* Intelligent Search Tool for Answering Clinical Questions. Develop new computational approaches to information retrieval that would allow a clinician or clinical researcher to pose a single query that would result in search of multiple data sources to produce a coherent response that highlights key relevant information which may signal new insights for clinical research or patient care. Information that could help a clinician diagnose or manage a health condition, or help a clinical researcher explore the significance of issues that arise during a clinical trial, is scattered across many different types of resources, such as paper or electronic charts, trial protocols, published biomedical articles, or best-practice guidelines for care. Develop artificial intelligence and information retrieval approaches that allow a clinician or researcher confronting complex patient problems to pose a single query that will result in a search that appears to "understand" the question, a search that inspects multiple databases and brings findings together into a useful answer. Clinical question answering (cQA) systems focus on the physician needs usually at the point of care, or the investigator in the lab. The questions usually asked either require information highly specific to their patient, e.g. the patient's lab results or previous history, answered by the patient's health record, or a more general type of information usually answered through generally available information sources. QA systems enhance the results of search engines by providing a concise summary of relevant information along with source hits. PubMed (http://www.ncbi.nlm.nih.gov/pubmed/) is the most ubiquitous biomedical search engine, however because it is a search engine the information retrieved is based on keyword searches and is not presented in a form for immediate consumption;the user has to drill down into the content of the webpages to find the facts/statements of interest. Moreover, the information that the clinician needs is likely to be of different types, for example a definition of a syndrome in combination with specific actions triggered by a particular diagnosis for a particular patient. Such information resides in different sources - encyclopedic and the EMR - and has to be dynamically accessed and presented to the user in an easily digestible format. We propose to develop a unified platform for clinical QA from multiple sources of clinical and biomedical narrative that implements semantic processing of the questions by fusing two existing technologies - the Mayo clinical Text Analysis and Knowledge Extraction System and the University of Colorado's Question Answering System. The specific research questions we are aiming to answer are: "How much effort is required to port a general semantic QA system to the clinical domain? How much additional domain-specific training is required? "What is the accuracy of such a system? Question Answering in the clinical domain is an emerging area of research. The challenges in the field are mainly attributed to the number of components that require domain specific training along with strict system requirements in terms of high precision and recall complemented by an accessible and user-friendly presentation. Our approach to overcome them is to re-use components already in place as part of Mayo clinical Text Analysis and Knowledge Extraction System and the University of Colorado's Question Answering System. Our approach is innovative in bringing together information from encyclopedic sources and the EMR to present it into a unified form to the clinician at the point of care or the investigator in the lab. The technology for that is based on semantic language processing which aims at "understanding" the meaning of the question and the narrative. Our proposed system holds the potential to impact quality of healthcare and translational research. Our approach is feasible because it uses content already in the EMR at the Mayo Clinic along with general medical knowledge from multiple readily-available resources. The proposed system will be built off mature and tested components allowing a fast and robust delivery cycle. Our unique integration of technologies together with sophisticated statistical machine learning algorithms applied to rich linguistic knowledge about events, contradictions, semantic structure, and question-types, will allow us to build a system which significantly extends the range of possible question types and responses available to clinicians, and seamlessly fuses these to generate a response. Our proposed work represents a high impact area that has the potential to improve healthcare delivery because it addresses needs that have been well-documented and studied (Ely et al., 2005). We aim to provide a unified multi-source solution for semantic retrieval, access and summarization of relevant information at the point of care or the lab. As such, the proposed cQA has the potential to play a vital and important decision- support role for the physician or the biomedical investigator. (max 2-3 sentences) Clinical question answering (cQA) systems focus on the physician needs usually at the point of care, or the investigator in the lab. The questions usually asked either require information highly specific to their patient, e.g. the patient's lab results or previous history, answered by the patient's health record, or a more general type of information usually answered through generally available information sources. Our proposed work to provide a unified multi-source solution for semantic retrieval, access and summarization of relevant information at the point of care or the lab, represents a high impact area that has the potential to improve healthcare delivery because it addresses needs that have been well-documented and studied.